1.0 Load required libraries

# Load all required packages, suppressing startup messages for clarity
suppressPackageStartupMessages({
  library(DiffBind)        # Differential binding analysis for ATAC/ChIP-seq data
  library(dplyr)           # Tidy data manipulation
  library(rtracklayer)     # Import/export of genomic ranges (e.g., GTF, BED)
  library(Rsamtools)       # Efficient BAM file access
  library(cowplot)         # Enhanced ggplot2 visualizations
  library(readxl)          # Reading Excel spreadsheets
  library(ggplot2)         # Core plotting system
  library(jsonlite)        # JSON parsing (for MultiQC reports)
  library(reshape2)        # Data transformation (wide <-> long)
  library(edgeR)           # RNA-seq/ATAC-seq count-based differential analysis
  library(DESeq2)          # Alternative to edgeR for count-based analysis
  library(csaw)            # Sliding window analysis of ChIP/ATAC-seq count data
  library(ChIPseeker)      # Peak annotation and visualization
  library(ChIPpeakAnno)    # Peak annotation for ChIP-seq data
  library(heatmaply)       # Interactive heatmaps using plotly
})

1.1 Read and format metadata

# Read metadata Excel file, skipping the first two rows:
# - First row: title/description
# - Second row: header to be assigned manually
raw <- read_excel("/mnt/IM/groupMansuy/anna/Bioinformatics_project/ATAC/2025_omniATAC_SCs_MSUS39F1-PND15-Adult_metadata.xlsx", col_names = FALSE, skip = 2)

# Assign proper column names from first actual row
colnames(raw) <- as.character(unlist(raw[1, ]))

# Remove the header row now stored as column names
metadata <- raw[-1, ]
metadata <- as.data.frame(metadata)
rownames(metadata) <- NULL
print(paste(colnames(metadata), collapse = ", "))
## [1] "Samples name, animal ID, Age, Condition, Cell type, Sorting strategy, Cell count, notes, Date tagmentation, Experimenter tagmentation, Date library prep, strain, DOB, protocol, notes, cycles PCR1, cycles PCR2, Concentration dilution 1:4 ng/µl (Qubit), Concentration ng/µl, Total volume µl, Primer i5 (hiseq/miseq), sequence i5, Primer i7, sequence i7, location"
print(dim(metadata))
## [1] 34 25

1.2 Construct sample file names

# Ensure 'Age' and 'Samples name' are stored as character vectors
metadata$Age <- as.character(metadata$Age)
metadata[["Samples name"]] <- as.character(metadata[["Samples name"]])

# Add age-based prefix for sample naming: 'ad' for Adult, 'P15' for PND15
metadata$AgePrefix <- ifelse(metadata$Age == "Adult", "ad", ifelse(metadata$Age == "PND15", "P15", NA))

# Construct file-compatible sample names (to match file system naming)
metadata$Sample_file_name <- paste0("M39F1", metadata$AgePrefix, "_", metadata[["Samples name"]])

1.3 Integrate multiQC and quality metrics

# Create phenotype table from metadata
pheno <- metadata
rownames(pheno) <- pheno$Sample_file_name

# Load MultiQC general statistics JSON from FastQC reports (trimmed FASTQ)
multiQC_fastQ <- fromJSON("/mnt/IM/groupMansuy/theresa/ATAC/MSUS39F1/results_mm10/02_qc_plots/02_FastQC_trimmed_data/multiqc_data/multiqc_data.json")$report_general_stats_data

# Clean sample names in the JSON list for easier lookup
names(multiQC_fastQ) <- gsub("results_mm10 | 02_qc_plots | 02_FastQC_trimmed_data | ", "", names(multiQC_fastQ), fixed = TRUE)

# Pre-allocate columns in phenotype table for FastQC metrics
pheno[, colnames(multiQC_fastQ[[1]])] <- NA

# Fill in FastQC metrics by matching sample ID (forward read file)
for (i in 1:nrow(pheno)) {
  id <- paste0(rownames(pheno)[i], "_1_val_1")
  pheno[i, colnames(multiQC_fastQ[[i]])] <- multiQC_fastQ[[grep(id, names(multiQC_fastQ))]]
}

1.4 Clean and add quality variables

# Use concentration as proxy for RNA library quality
pheno$RNA.conc <- pheno[, "Concentration ng/µl"]

# Convert relevant variables to numeric for downstream analysis
pheno[c("Cell count", "cycles PCR1", "cycles PCR2", "Concentration dilution 1:4 ng/µl (Qubit)", "Total volume µl", "RNA.conc")] <- 
  lapply(pheno[c("Cell count", "cycles PCR1", "cycles PCR2", "Concentration dilution 1:4 ng/µl (Qubit)", "Total volume µl", "RNA.conc")], as.numeric)

1.5 Merge TSS and PBC metrics

# TSS score measures enrichment at transcription start sites (expected in high-quality ATAC)
tss <- read.table("/mnt/IM/groupMansuy/theresa/ATAC/MSUS39F1/results_mm10/02_qc_plots/04_tssscores/combined_TSS_Scores_allsamples.txt", header = TRUE, sep = "\t")
tss$Sample <- sub("\\.sorted$", "", tss$Sample)  # Remove file suffix for matching
colnames(tss)[colnames(tss) == "Sample"] <- "Sample_file_name"
pheno <- merge(pheno, tss, by = "Sample_file_name", all.x = TRUE)

# NRF = Non-redundant fraction
# PBC1/2 = PCR bottleneck coefficients (1 = ideal, <0.5 = poor complexity)
pbc <- read.table("/mnt/IM/groupMansuy/theresa/ATAC/MSUS39F1/results_mm10/02_qc_plots/10_pbc_metrics/combined_PBC_Metrics.txt", header = TRUE, sep = "\t")
colnames(pbc)[colnames(pbc) == "Sample"] <- "Sample_file_name"
pheno <- merge(pheno, pbc, by = "Sample_file_name", all.x = TRUE)

# Final sample identifier column
pheno$Sample <- pheno$Sample_file_name

# Save final phenotype table to CSV
write.csv(pheno, "/mnt/IM/groupMansuy/anna/Bioinformatics_project/ATAC/pheno.csv", row.names = FALSE)

1.6 Load cleaned phenotype data

# Reload saved phenotype table (in case of workflow split)
pheno <- read.csv("/mnt/IM/groupMansuy/anna/Bioinformatics_project/ATAC/pheno.csv")
rownames(pheno) <- pheno$Sample

1.7 Correlation plot: all samples

# Define technical and QC variables to inspect
PCAvariables <- c("cycles.PCR1", "cycles.PCR2", "TSS_Score", "NRF", "PBC1", "PBC2", "RNA.conc", colnames(multiQC_fastQ[[1]]))

## Interactive heatmap: all samples
# Retain only numeric variables with non-zero variance
valid_vars <- PCAvariables[sapply(pheno[, PCAvariables], function(x) is.numeric(x) && sd(x, na.rm = TRUE) > 0)]

# Create interactive heatmap for visual correlation inspection
heatmaply_cor(x = cor(pheno[, valid_vars], use = "pairwise.complete.obs"), xlab = "Features", ylab = "Features", show_dendrogram = c(TRUE, FALSE), plot_method = "plotly", file = "/mnt/groupMansuy/anna/Bioinformatics_project/ATAC/results/tec_vars_03/TechnicalVariablesCorrelationsHeatmaply.html")

1.8 Correlation: PND15 (Pups)

# Define valid variables again for PND15 samples only
valid_vars_pups <- PCAvariables[sapply(pheno[pheno$Age == "PND15", PCAvariables], function(x) {
  is.numeric(x) && sd(x, na.rm = TRUE) > 0 && !all(is.na(x))
})]

# Interactive version
heatmaply_cor(x = cor(pheno[pheno$Age == "PND15", valid_vars_pups], use = "pairwise.complete.obs"), xlab = "Features", ylab = "Features", show_dendrogram = c(TRUE, FALSE), plot_method = "plotly", file = "/mnt/groupMansuy/anna/Bioinformatics_project/ATAC/results/tec_vars_03/TechnicalVariablesCorrelationsHeatmaply_only_pups.html")

1.9 Correlation: Adults

# Define valid variables again for adults samples only
valid_vars_adults <- PCAvariables[sapply(pheno[pheno$Age == "Adult", PCAvariables], function(x) {
  is.numeric(x) && sd(x, na.rm = TRUE) > 0 && !all(is.na(x))
})]

# Interactive version
heatmaply_cor(x = cor(pheno[pheno$Age == "Adult", valid_vars_adults], use = "pairwise.complete.obs"), xlab = "Features", ylab = "Features", show_dendrogram = c(TRUE, FALSE), plot_method = "plotly", file = "/mnt/groupMansuy/anna/Bioinformatics_project/ATAC/results/tec_vars_03/TechnicalVariablesCorrelationsHeatmaply_only_adults.html")

1.10 Interpretation

The correlation analysis of technical quality control (QC) metrics across all samples, as well as stratified by age group (PND15 pups and adults), provides insight into the consistency and potential confounding variables within the ATAC-seq data.
Across the entire dataset, several moderate to strong correlations are observed between metrics related to library complexity and sequencing quality. Notably, PBC1 and NRF, which both assess library redundancy, show strong positive correlation as expected, reflecting that higher non-redundant fractions co-occur with reduced PCR bottlenecking. There is also a clear inverse relationship between percent duplicates and NRF/PBC1, confirming that more duplication is associated with lower library complexity.
In the PND15 subset, correlations largely mirror those of the full dataset but appear slightly weaker overall, possibly due to smaller sample size or more homogeneous technical conditions. Interestingly, TSS enrichment score (a proxy for signal-to-noise) is only modestly correlated with most library metrics, suggesting that chromatin accessibility signal quality is relatively independent of metrics like input concentration or PCR cycles in these samples.
For adult samples, the same general patterns hold, but correlations between TSS score and both PBC1 and NRF are slightly stronger than in pups. This might suggest that in adult spermatogonial stem cells, overall chromatin signal quality is more tightly linked to library complexity than in early developmental stages.
These findings emphasize that certain QC metrics—especially duplication rate, NRF, and PBC1—are redundant and tightly coupled, while TSS enrichment, GC content, and RNA concentration vary more independently and may better reflect biological variation or technical noise. These relationships can help guide the choice of covariates in downstream normalization or correction steps, especially if differential accessibility.
To account for unwanted technical and biological variation during downstream differential accessibility analysis, we plan to apply a Surrogate Variable Analysis (SVA) approach. This unsupervised method helps identify latent sources of variation that may or may not be captured by known covariates, providing a robust correction strategy that avoids overfitting to technical metrics alone. This will be especially useful to ensure that observed differences in chromatin accessibility reflect the biological effects of early life stress, rather than residual technical or confounding biases. To evaluate the effectiveness of this correction, we will assess the correlation between principal components of the accessibility matrix (e.g., read counts over peaks) and known covariates before and after applying SVA, which will help determine whether unwanted variation has been reduced without masking biological signals.